The proper choice of infrastructure also plays a key role in determining the efficiency of the organization's data science platform. Too little, and the algorithms will take too long to execute; too much and you may have a lot of resources remaining unutilized. As such, the latter is preferable to having too little, which thwarts progress and the ability of any machine learning researcher to efficiently perform his or her tasks.
Over the past 5 - 7 years, organizations have gradually shifted their resources to cloud-based platforms such as Amazon Web Services, Microsoft Azure, and Google Compute Engine. Today, all of these contain extremely sophisticated and extensive architecture to support machine learning, data mining, and in general data science at an enterprise level to meet the needs of organizations of all sizes.
In addition, the concept of images, such as AMI images in Amazon's AWS, allows users to initiate a pre-built snapshot of...
